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VisionSR

github.com

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Spec score 7.9 #54 of 99 in Image Upscaling Software

Spec sheet

Purpose
VisionSR is an AI super-resolution engine for image upscaling, restoration, face recovery, and detail reconstruction.Read 12 Oct 2026 · github.com
Intended users
The project describes a 4 GB laptop GPU as its design target and includes model paths for photographs, portraits, scans, and line art.Read 12 Oct 2026 · github.com
API
The HTTP API includes image analysis, enhancement jobs, live progress events over SSE, and job results.Read 12 Oct 2026 · github.com
Integrations
The README documents PyTorch, ONNX Runtime, DirectML, and OpenVINO as supported inference backends.Read 12 Oct 2026 · github.com
Security
The repository README lists CI security workflows for CVEs, CodeQL, secrets, and filesystem checks.Read 12 Oct 2026 · github.com
License
The project is licensed under Apache-2.0, and the README says model weights have their own licenses and are downloaded rather than redistributed.Read 12 Oct 2026 · github.com
API access
YesRead 22 Sep 2026 · github.com
Automatic routing
It analyzes image content and damage, then automatically selects a suitable model and tile size based on available VRAM.Read 12 Oct 2026 · github.com
Batch processing
YesRead 22 Sep 2026 · github.com
Desktop app
YesRead 22 Sep 2026 · github.com
Desktop features
The desktop app includes a watched hot folder and native open and save, and starts its own engine process.Read 12 Oct 2026 · github.com
Hardware
The README describes tiled inference designed for a 4 GB GPU; GPU installation requires an NVIDIA card, while the packaged ONNX Runtime and DirectML app is described as supporting DX12 GPUs from NVIDIA, AMD, or Intel.Read 12 Oct 2026 · github.com
Hardware requirements
The project requires Python 3.11 or later, and an NVIDIA card for GPU use; tiled inference is designed to work from 4 GB VRAM.Read 12 Oct 2026 · github.com
Image tools
Listed capabilities include super resolution, face restoration, and background removal.Read 12 Oct 2026 · github.com
Image workflows
Features include batch folder processing, face restoration, background removal, image analysis, and model inspection.Read 12 Oct 2026 · github.com
Interfaces
It provides a CLI, Python interface, HTTP API, web UI, and Electron desktop app.Read 12 Oct 2026 · github.com
Limits
The README says the training pipeline and cloud or multi-tenant layer are not built.Read 12 Oct 2026 · github.com
Local processing
The project says images are processed locally, with no upload, per-image cost, or queue.Read 12 Oct 2026 · github.com
Maximum scale
4xRead 22 Sep 2026 · github.com
Model selection
Its content classification and model selection use inspectable heuristic rules and image statistics rather than a learned classifier.Read 12 Oct 2026 · github.com
Models
The README lists nine models for super resolution, anime and illustration upscaling, face restoration, background removal, and face detection.Read 12 Oct 2026 · github.com
Notable limits
The README says the training pipeline and cloud or multi-tenant layer are not built.Read 12 Oct 2026 · github.com
Runtime options
The documented backends include PyTorch, ONNX Runtime, DirectML, and OpenVINO; the README says the shipped desktop runtime uses ONNX and can run through DirectML on DX12 GPUs.Read 12 Oct 2026 · github.com
Security checks
The repository README lists CI security workflows for CVEs, CodeQL, secrets, and filesystem checks.Read 12 Oct 2026 · github.com
Upscaling method
aiRead 22 Sep 2026 · github.com
Windows packaging
The README documents a Windows x64 installer and says the ONNX Runtime plus DirectML package is about 250 MB and supports DX12 GPUs from NVIDIA, AMD, or Intel.Read 12 Oct 2026 · github.com

Where Specifiction places it

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